Overview

Dataset statistics

Number of variables26
Number of observations20631
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 MiB
Average record size in memory208.0 B

Variable types

Numeric18
Categorical8

Alerts

setting_3 has constant value ""Constant
(Total temperature at fan inlet) (â—¦R) has constant value ""Constant
(Pressure at fan inlet) (psia) has constant value ""Constant
(Engine pressure ratio(P50/P2) has constant value ""Constant
(Burner fuel-air ratio) has constant value ""Constant
(Demanded fan speed) has constant value ""Constant
(Demanded corrected fan speed) has constant value ""Constant
time_cycles is highly overall correlated with (Total temperature at LPC outlet) (â—¦R) and 9 other fieldsHigh correlation
(Total temperature at LPC outlet) (â—¦R) is highly overall correlated with time_cycles and 11 other fieldsHigh correlation
(Total temperature at HPC outlet) (â—¦R) is highly overall correlated with time_cycles and 11 other fieldsHigh correlation
(Total temperature at LPT outlet) (â—¦R) is highly overall correlated with time_cycles and 11 other fieldsHigh correlation
(Total pressure at HPC outlet) (psia) is highly overall correlated with time_cycles and 11 other fieldsHigh correlation
(Physical fan speed) (rpm) is highly overall correlated with (Total temperature at LPC outlet) (â—¦R) and 10 other fieldsHigh correlation
(Physical core speed) (rpm) is highly overall correlated with (Corrected core speed) (rpm)High correlation
(Static pressure at HPC outlet) (psia) is highly overall correlated with time_cycles and 11 other fieldsHigh correlation
(Ratio of fuel flow to Ps30) (pps/psia) is highly overall correlated with time_cycles and 11 other fieldsHigh correlation
(Corrected fan speed) (rpm) is highly overall correlated with (Total temperature at LPC outlet) (â—¦R) and 10 other fieldsHigh correlation
(Corrected core speed) (rpm) is highly overall correlated with (Physical core speed) (rpm)High correlation
(Bypass Ratio) is highly overall correlated with time_cycles and 11 other fieldsHigh correlation
(Bleed Enthalpy) is highly overall correlated with time_cycles and 11 other fieldsHigh correlation
(HPT coolant bleed (lbm/s)) is highly overall correlated with time_cycles and 11 other fieldsHigh correlation
(LPT coolant bleed (lbm/s)) is highly overall correlated with time_cycles and 11 other fieldsHigh correlation
(Total pressure in bypass-duct) (psia) is highly imbalanced (86.0%)Imbalance
setting_1 has 413 (2.0%) zerosZeros
setting_2 has 2070 (10.0%) zerosZeros

Reproduction

Analysis started2023-03-14 20:23:57.173074
Analysis finished2023-03-14 20:24:55.072124
Duration57.9 seconds
Software versionydata-profiling vv4.1.0
Download configurationconfig.json

Variables

unit_number
Real number (ℝ)

Distinct100
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.506568
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-03-14T16:24:55.257981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q126
median52
Q377
95-th percentile96
Maximum100
Range99
Interquartile range (IQR)51

Descriptive statistics

Standard deviation29.227633
Coefficient of variation (CV)0.56745449
Kurtosis-1.2198241
Mean51.506568
Median Absolute Deviation (MAD)26
Skewness-0.067815234
Sum1062632
Variance854.25453
MonotonicityIncreasing
2023-03-14T16:24:55.412738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 362
 
1.8%
92 341
 
1.7%
96 336
 
1.6%
67 313
 
1.5%
83 293
 
1.4%
2 287
 
1.4%
95 283
 
1.4%
64 283
 
1.4%
86 278
 
1.3%
17 276
 
1.3%
Other values (90) 17579
85.2%
ValueCountFrequency (%)
1 192
0.9%
2 287
1.4%
3 179
0.9%
4 189
0.9%
5 269
1.3%
6 188
0.9%
7 259
1.3%
8 150
0.7%
9 201
1.0%
10 222
1.1%
ValueCountFrequency (%)
100 200
1.0%
99 185
0.9%
98 156
0.8%
97 202
1.0%
96 336
1.6%
95 283
1.4%
94 258
1.3%
93 155
0.8%
92 341
1.7%
91 135
 
0.7%

time_cycles
Real number (ℝ)

Distinct362
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.80786
Minimum1
Maximum362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-03-14T16:24:55.588152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q152
median104
Q3156
95-th percentile230
Maximum362
Range361
Interquartile range (IQR)104

Descriptive statistics

Standard deviation68.88099
Coefficient of variation (CV)0.63305159
Kurtosis-0.2185391
Mean108.80786
Median Absolute Deviation (MAD)52
Skewness0.49990397
Sum2244815
Variance4744.5908
MonotonicityNot monotonic
2023-03-14T16:24:55.736555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 100
 
0.5%
66 100
 
0.5%
97 100
 
0.5%
96 100
 
0.5%
95 100
 
0.5%
94 100
 
0.5%
93 100
 
0.5%
91 100
 
0.5%
90 100
 
0.5%
89 100
 
0.5%
Other values (352) 19631
95.2%
ValueCountFrequency (%)
1 100
0.5%
2 100
0.5%
3 100
0.5%
4 100
0.5%
5 100
0.5%
6 100
0.5%
7 100
0.5%
8 100
0.5%
9 100
0.5%
10 100
0.5%
ValueCountFrequency (%)
362 1
< 0.1%
361 1
< 0.1%
360 1
< 0.1%
359 1
< 0.1%
358 1
< 0.1%
357 1
< 0.1%
356 1
< 0.1%
355 1
< 0.1%
354 1
< 0.1%
353 1
< 0.1%

setting_1
Real number (ℝ)

Distinct158
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.8701469 × 10-6
Minimum-0.0087
Maximum0.0087
Zeros413
Zeros (%)2.0%
Negative10061
Negative (%)48.8%
Memory size161.3 KiB
2023-03-14T16:24:55.951163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0087
5-th percentile-0.0037
Q1-0.0015
median0
Q30.0015
95-th percentile0.0036
Maximum0.0087
Range0.0174
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.0021873134
Coefficient of variation (CV)-246.5927
Kurtosis-0.0091316243
Mean-8.8701469 × 10-6
Median Absolute Deviation (MAD)0.0015
Skewness-0.024766267
Sum-0.183
Variance4.7843401 × 10-6
MonotonicityNot monotonic
2023-03-14T16:24:56.198173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 413
 
2.0%
0.0002 398
 
1.9%
0.0004 394
 
1.9%
-0.0005 390
 
1.9%
0.0001 382
 
1.9%
0.0005 381
 
1.8%
0.0006 379
 
1.8%
-0.0006 375
 
1.8%
0.0003 364
 
1.8%
0.0009 362
 
1.8%
Other values (148) 16793
81.4%
ValueCountFrequency (%)
-0.0087 1
 
< 0.1%
-0.0086 1
 
< 0.1%
-0.0084 1
 
< 0.1%
-0.0081 2
< 0.1%
-0.0078 1
 
< 0.1%
-0.0075 1
 
< 0.1%
-0.0074 3
< 0.1%
-0.0073 1
 
< 0.1%
-0.0072 2
< 0.1%
-0.007 2
< 0.1%
ValueCountFrequency (%)
0.0087 1
 
< 0.1%
0.0083 1
 
< 0.1%
0.0077 1
 
< 0.1%
0.0076 1
 
< 0.1%
0.0074 3
< 0.1%
0.0073 1
 
< 0.1%
0.0072 4
< 0.1%
0.0071 2
< 0.1%
0.007 2
< 0.1%
0.0069 2
< 0.1%

setting_2
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3508313 × 10-6
Minimum-0.0006
Maximum0.0006
Zeros2070
Zeros (%)10.0%
Negative9225
Negative (%)44.7%
Memory size161.3 KiB
2023-03-14T16:24:56.353363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-0.0006
5-th percentile-0.0004
Q1-0.0002
median0
Q30.0003
95-th percentile0.0005
Maximum0.0006
Range0.0012
Interquartile range (IQR)0.0005

Descriptive statistics

Standard deviation0.00029306212
Coefficient of variation (CV)124.66319
Kurtosis-1.130447
Mean2.3508313 × 10-6
Median Absolute Deviation (MAD)0.0003
Skewness0.0090851197
Sum0.0485
Variance8.5885409 × 10-8
MonotonicityNot monotonic
2023-03-14T16:24:56.459685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
-0.0003 2104
10.2%
0.0001 2097
10.2%
0 2070
10.0%
0.0003 2065
10.0%
-0.0004 2051
9.9%
-0.0002 2049
9.9%
0.0002 2038
9.9%
-0.0001 2029
9.8%
0.0004 1997
9.7%
0.0005 1068
5.2%
Other values (3) 1063
5.2%
ValueCountFrequency (%)
-0.0006 34
 
0.2%
-0.0005 958
4.6%
-0.0004 2051
9.9%
-0.0003 2104
10.2%
-0.0002 2049
9.9%
-0.0001 2029
9.8%
0 2070
10.0%
0.0001 2097
10.2%
0.0002 2038
9.9%
0.0003 2065
10.0%
ValueCountFrequency (%)
0.0006 71
 
0.3%
0.0005 1068
5.2%
0.0004 1997
9.7%
0.0003 2065
10.0%
0.0002 2038
9.9%
0.0001 2097
10.2%
0 2070
10.0%
-0.0001 2029
9.8%
-0.0002 2049
9.9%
-0.0003 2104
10.2%

setting_3
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
100.0
20631 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters103155
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row100.0

Common Values

ValueCountFrequency (%)
100.0 20631
100.0%

Length

2023-03-14T16:24:56.569332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:24:56.688771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
100.0 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82524
80.0%
Other Punctuation 20631
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 61893
75.0%
1 20631
 
25.0%
Other Punctuation
ValueCountFrequency (%)
. 20631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103155
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
518.67
20631 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters123786
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row518.67
2nd row518.67
3rd row518.67
4th row518.67
5th row518.67

Common Values

ValueCountFrequency (%)
518.67 20631
100.0%

Length

2023-03-14T16:24:56.781270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:24:56.885970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
518.67 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
5 20631
16.7%
1 20631
16.7%
8 20631
16.7%
. 20631
16.7%
6 20631
16.7%
7 20631
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 103155
83.3%
Other Punctuation 20631
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 20631
20.0%
1 20631
20.0%
8 20631
20.0%
6 20631
20.0%
7 20631
20.0%
Other Punctuation
ValueCountFrequency (%)
. 20631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 123786
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 20631
16.7%
1 20631
16.7%
8 20631
16.7%
. 20631
16.7%
6 20631
16.7%
7 20631
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123786
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 20631
16.7%
1 20631
16.7%
8 20631
16.7%
. 20631
16.7%
6 20631
16.7%
7 20631
16.7%
Distinct310
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean642.68093
Minimum641.21
Maximum644.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-03-14T16:24:57.181731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum641.21
5-th percentile641.92
Q1642.325
median642.64
Q3643
95-th percentile643.58
Maximum644.53
Range3.32
Interquartile range (IQR)0.675

Descriptive statistics

Standard deviation0.50005327
Coefficient of variation (CV)0.00077807392
Kurtosis-0.11204294
Mean642.68093
Median Absolute Deviation (MAD)0.34
Skewness0.31652589
Sum13259150
Variance0.25005327
MonotonicityNot monotonic
2023-03-14T16:24:57.334013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
642.5 190
 
0.9%
642.56 189
 
0.9%
642.53 188
 
0.9%
642.6 184
 
0.9%
642.67 179
 
0.9%
642.44 175
 
0.8%
642.63 175
 
0.8%
642.57 172
 
0.8%
642.64 168
 
0.8%
642.73 167
 
0.8%
Other values (300) 18844
91.3%
ValueCountFrequency (%)
641.21 1
 
< 0.1%
641.25 2
< 0.1%
641.27 3
< 0.1%
641.3 4
< 0.1%
641.31 1
 
< 0.1%
641.32 2
< 0.1%
641.33 2
< 0.1%
641.34 1
 
< 0.1%
641.35 1
 
< 0.1%
641.36 2
< 0.1%
ValueCountFrequency (%)
644.53 2
< 0.1%
644.5 1
< 0.1%
644.47 1
< 0.1%
644.44 1
< 0.1%
644.39 1
< 0.1%
644.37 1
< 0.1%
644.35 1
< 0.1%
644.34 1
< 0.1%
644.31 1
< 0.1%
644.3 2
< 0.1%
Distinct3012
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1590.5231
Minimum1571.04
Maximum1616.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-03-14T16:24:57.505532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1571.04
5-th percentile1581.11
Q11586.26
median1590.1
Q31594.38
95-th percentile1601.47
Maximum1616.91
Range45.87
Interquartile range (IQR)8.12

Descriptive statistics

Standard deviation6.1311495
Coefficient of variation (CV)0.0038548006
Kurtosis0.0077618224
Mean1590.5231
Median Absolute Deviation (MAD)4.05
Skewness0.30894581
Sum32814082
Variance37.590994
MonotonicityNot monotonic
2023-03-14T16:24:57.648137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1590.1 27
 
0.1%
1589.76 26
 
0.1%
1589.98 25
 
0.1%
1592.11 25
 
0.1%
1587.86 24
 
0.1%
1584.95 23
 
0.1%
1590.54 23
 
0.1%
1589.08 23
 
0.1%
1589.44 23
 
0.1%
1587.82 22
 
0.1%
Other values (3002) 20390
98.8%
ValueCountFrequency (%)
1571.04 1
< 0.1%
1571.06 1
< 0.1%
1571.84 1
< 0.1%
1571.99 1
< 0.1%
1572.34 1
< 0.1%
1572.4 1
< 0.1%
1572.46 1
< 0.1%
1572.67 1
< 0.1%
1572.76 1
< 0.1%
1572.98 1
< 0.1%
ValueCountFrequency (%)
1616.91 1
< 0.1%
1614.93 1
< 0.1%
1614.72 1
< 0.1%
1613.62 1
< 0.1%
1613.29 1
< 0.1%
1612.88 1
< 0.1%
1612.63 1
< 0.1%
1612.11 1
< 0.1%
1611.92 1
< 0.1%
1611.57 1
< 0.1%
Distinct4051
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1408.9338
Minimum1382.25
Maximum1441.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-03-14T16:24:57.802836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1382.25
5-th percentile1395.62
Q11402.36
median1408.04
Q31414.555
95-th percentile1425.67
Maximum1441.49
Range59.24
Interquartile range (IQR)12.195

Descriptive statistics

Standard deviation9.0006048
Coefficient of variation (CV)0.0063882383
Kurtosis-0.16368086
Mean1408.9338
Median Absolute Deviation (MAD)6.04
Skewness0.44319434
Sum29067713
Variance81.010886
MonotonicityNot monotonic
2023-03-14T16:24:57.941594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1409.01 20
 
0.1%
1404.47 18
 
0.1%
1407.15 18
 
0.1%
1407.02 18
 
0.1%
1414.03 18
 
0.1%
1410.54 18
 
0.1%
1403.23 17
 
0.1%
1407.18 16
 
0.1%
1410.57 16
 
0.1%
1401.27 16
 
0.1%
Other values (4041) 20456
99.2%
ValueCountFrequency (%)
1382.25 1
< 0.1%
1385.19 1
< 0.1%
1385.75 1
< 0.1%
1386.29 1
< 0.1%
1386.43 1
< 0.1%
1386.69 1
< 0.1%
1387.16 1
< 0.1%
1387.36 1
< 0.1%
1387.38 1
< 0.1%
1387.5 1
< 0.1%
ValueCountFrequency (%)
1441.49 1
< 0.1%
1438.96 1
< 0.1%
1438.51 1
< 0.1%
1438.41 1
< 0.1%
1438.22 1
< 0.1%
1438.16 1
< 0.1%
1438.1 1
< 0.1%
1437.98 1
< 0.1%
1437.88 1
< 0.1%
1437.81 1
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
14.62
20631 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters103155
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14.62
2nd row14.62
3rd row14.62
4th row14.62
5th row14.62

Common Values

ValueCountFrequency (%)
14.62 20631
100.0%

Length

2023-03-14T16:24:58.067443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:24:58.184135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
14.62 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
1 20631
20.0%
4 20631
20.0%
. 20631
20.0%
6 20631
20.0%
2 20631
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82524
80.0%
Other Punctuation 20631
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20631
25.0%
4 20631
25.0%
6 20631
25.0%
2 20631
25.0%
Other Punctuation
ValueCountFrequency (%)
. 20631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103155
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 20631
20.0%
4 20631
20.0%
. 20631
20.0%
6 20631
20.0%
2 20631
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 20631
20.0%
4 20631
20.0%
. 20631
20.0%
6 20631
20.0%
2 20631
20.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
21.61
20225 
21.6
 
406

Length

Max length5
Median length5
Mean length4.9803209
Min length4

Characters and Unicode

Total characters102749
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21.61
2nd row21.61
3rd row21.61
4th row21.61
5th row21.61

Common Values

ValueCountFrequency (%)
21.61 20225
98.0%
21.6 406
 
2.0%

Length

2023-03-14T16:24:58.274871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:24:58.386569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
21.61 20225
98.0%
21.6 406
 
2.0%

Most occurring characters

ValueCountFrequency (%)
1 40856
39.8%
2 20631
20.1%
. 20631
20.1%
6 20631
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82118
79.9%
Other Punctuation 20631
 
20.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 40856
49.8%
2 20631
25.1%
6 20631
25.1%
Other Punctuation
ValueCountFrequency (%)
. 20631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 102749
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 40856
39.8%
2 20631
20.1%
. 20631
20.1%
6 20631
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 102749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 40856
39.8%
2 20631
20.1%
. 20631
20.1%
6 20631
20.1%
Distinct513
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean553.36771
Minimum549.85
Maximum556.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-03-14T16:24:58.496218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum549.85
5-th percentile551.74
Q1552.81
median553.44
Q3554.01
95-th percentile554.69
Maximum556.06
Range6.21
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.88509226
Coefficient of variation (CV)0.0015994649
Kurtosis-0.15794922
Mean553.36771
Median Absolute Deviation (MAD)0.6
Skewness-0.39432894
Sum11416529
Variance0.7833883
MonotonicityNot monotonic
2023-03-14T16:24:58.635094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
553.62 116
 
0.6%
553.76 115
 
0.6%
553.72 110
 
0.5%
553.94 110
 
0.5%
553.43 108
 
0.5%
553.74 107
 
0.5%
553.75 106
 
0.5%
554 105
 
0.5%
553.9 104
 
0.5%
553.52 103
 
0.5%
Other values (503) 19547
94.7%
ValueCountFrequency (%)
549.85 1
< 0.1%
550.34 1
< 0.1%
550.35 1
< 0.1%
550.42 1
< 0.1%
550.43 1
< 0.1%
550.48 2
< 0.1%
550.49 1
< 0.1%
550.5 1
< 0.1%
550.51 2
< 0.1%
550.52 1
< 0.1%
ValueCountFrequency (%)
556.06 1
< 0.1%
555.86 1
< 0.1%
555.72 1
< 0.1%
555.7 1
< 0.1%
555.67 1
< 0.1%
555.66 1
< 0.1%
555.64 1
< 0.1%
555.61 1
< 0.1%
555.6 1
< 0.1%
555.58 1
< 0.1%
Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.0967
Minimum2387.9
Maximum2388.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-03-14T16:24:58.786655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2387.9
5-th percentile2387.99
Q12388.05
median2388.09
Q32388.14
95-th percentile2388.22
Maximum2388.56
Range0.66
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.070985479
Coefficient of variation (CV)2.9724709 × 10-5
Kurtosis0.33314901
Mean2388.0967
Median Absolute Deviation (MAD)0.05
Skewness0.47941086
Sum49268822
Variance0.0050389382
MonotonicityNot monotonic
2023-03-14T16:24:58.930399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2388.11 1181
 
5.7%
2388.1 1159
 
5.6%
2388.09 1149
 
5.6%
2388.08 1126
 
5.5%
2388.07 1077
 
5.2%
2388.12 1069
 
5.2%
2388.06 1050
 
5.1%
2388.13 1033
 
5.0%
2388.05 1013
 
4.9%
2388.04 910
 
4.4%
Other values (43) 9864
47.8%
ValueCountFrequency (%)
2387.9 1
 
< 0.1%
2387.91 3
 
< 0.1%
2387.92 9
 
< 0.1%
2387.93 16
 
0.1%
2387.94 33
 
0.2%
2387.95 72
 
0.3%
2387.96 145
 
0.7%
2387.97 201
1.0%
2387.98 339
1.6%
2387.99 426
2.1%
ValueCountFrequency (%)
2388.56 1
 
< 0.1%
2388.52 1
 
< 0.1%
2388.5 1
 
< 0.1%
2388.46 1
 
< 0.1%
2388.44 2
 
< 0.1%
2388.37 1
 
< 0.1%
2388.36 1
 
< 0.1%
2388.35 2
 
< 0.1%
2388.34 13
0.1%
2388.33 13
0.1%
Distinct6403
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9065.2429
Minimum9021.73
Maximum9244.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-03-14T16:24:59.079072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9021.73
5-th percentile9042.55
Q19053.1
median9060.66
Q39069.42
95-th percentile9109.98
Maximum9244.59
Range222.86
Interquartile range (IQR)16.32

Descriptive statistics

Standard deviation22.08288
Coefficient of variation (CV)0.0024359942
Kurtosis9.3786813
Mean9065.2429
Median Absolute Deviation (MAD)8.13
Skewness2.5553649
Sum1.8702503 × 108
Variance487.65357
MonotonicityNot monotonic
2023-03-14T16:24:59.222730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9058.88 16
 
0.1%
9060.37 15
 
0.1%
9060.55 15
 
0.1%
9056.86 15
 
0.1%
9063.22 15
 
0.1%
9060.87 15
 
0.1%
9054.54 14
 
0.1%
9061.05 14
 
0.1%
9057.95 14
 
0.1%
9065.47 14
 
0.1%
Other values (6393) 20484
99.3%
ValueCountFrequency (%)
9021.73 1
< 0.1%
9023.85 1
< 0.1%
9024.27 1
< 0.1%
9024.42 1
< 0.1%
9025.22 1
< 0.1%
9025.29 1
< 0.1%
9026.08 1
< 0.1%
9026.17 1
< 0.1%
9026.19 1
< 0.1%
9026.66 1
< 0.1%
ValueCountFrequency (%)
9244.59 1
< 0.1%
9239.76 1
< 0.1%
9228.53 1
< 0.1%
9226.6 1
< 0.1%
9224.87 1
< 0.1%
9224.53 1
< 0.1%
9223.56 1
< 0.1%
9221.31 1
< 0.1%
9220.88 1
< 0.1%
9219.81 1
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
1.3
20631 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters61893
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.3
2nd row1.3
3rd row1.3
4th row1.3
5th row1.3

Common Values

ValueCountFrequency (%)
1.3 20631
100.0%

Length

2023-03-14T16:24:59.349496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:24:59.451515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.3 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
1 20631
33.3%
. 20631
33.3%
3 20631
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41262
66.7%
Other Punctuation 20631
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 20631
50.0%
3 20631
50.0%
Other Punctuation
ValueCountFrequency (%)
. 20631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61893
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 20631
33.3%
. 20631
33.3%
3 20631
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 20631
33.3%
. 20631
33.3%
3 20631
33.3%
Distinct159
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.541168
Minimum46.85
Maximum48.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-03-14T16:24:59.557628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum46.85
5-th percentile47.15
Q147.35
median47.51
Q347.7
95-th percentile48.045
Maximum48.53
Range1.68
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.2670874
Coefficient of variation (CV)0.0056180235
Kurtosis-0.17219188
Mean47.541168
Median Absolute Deviation (MAD)0.18
Skewness0.46932909
Sum980821.84
Variance0.071335679
MonotonicityNot monotonic
2023-03-14T16:24:59.697042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.46 341
 
1.7%
47.57 338
 
1.6%
47.49 332
 
1.6%
47.45 332
 
1.6%
47.47 331
 
1.6%
47.52 326
 
1.6%
47.37 321
 
1.6%
47.48 319
 
1.5%
47.44 318
 
1.5%
47.43 311
 
1.5%
Other values (149) 17362
84.2%
ValueCountFrequency (%)
46.85 1
 
< 0.1%
46.86 3
< 0.1%
46.88 2
 
< 0.1%
46.89 1
 
< 0.1%
46.9 1
 
< 0.1%
46.91 1
 
< 0.1%
46.92 3
< 0.1%
46.93 3
< 0.1%
46.94 6
< 0.1%
46.95 6
< 0.1%
ValueCountFrequency (%)
48.53 1
 
< 0.1%
48.52 1
 
< 0.1%
48.48 1
 
< 0.1%
48.43 1
 
< 0.1%
48.41 4
< 0.1%
48.4 4
< 0.1%
48.39 3
< 0.1%
48.38 1
 
< 0.1%
48.37 2
 
< 0.1%
48.35 5
< 0.1%
Distinct427
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521.41347
Minimum518.69
Maximum523.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-03-14T16:24:59.855782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum518.69
5-th percentile520.04
Q1520.96
median521.48
Q3521.95
95-th percentile522.5
Maximum523.38
Range4.69
Interquartile range (IQR)0.99

Descriptive statistics

Standard deviation0.73755339
Coefficient of variation (CV)0.0014145269
Kurtosis-0.14491657
Mean521.41347
Median Absolute Deviation (MAD)0.5
Skewness-0.44240724
Sum10757281
Variance0.54398501
MonotonicityNot monotonic
2023-03-14T16:25:00.190470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521.63 143
 
0.7%
521.42 136
 
0.7%
521.35 131
 
0.6%
521.56 129
 
0.6%
521.66 126
 
0.6%
521.54 125
 
0.6%
521.69 124
 
0.6%
521.5 123
 
0.6%
521.46 121
 
0.6%
521.43 121
 
0.6%
Other values (417) 19352
93.8%
ValueCountFrequency (%)
518.69 1
< 0.1%
518.83 2
< 0.1%
518.94 1
< 0.1%
518.95 1
< 0.1%
518.98 1
< 0.1%
518.99 1
< 0.1%
519.01 1
< 0.1%
519.02 1
< 0.1%
519.03 1
< 0.1%
519.06 2
< 0.1%
ValueCountFrequency (%)
523.38 2
< 0.1%
523.35 1
< 0.1%
523.31 1
< 0.1%
523.27 1
< 0.1%
523.26 2
< 0.1%
523.25 1
< 0.1%
523.24 1
< 0.1%
523.23 1
< 0.1%
523.21 1
< 0.1%
523.2 1
< 0.1%
Distinct56
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2388.0962
Minimum2387.88
Maximum2388.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-03-14T16:25:00.345048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2387.88
5-th percentile2387.99
Q12388.04
median2388.09
Q32388.14
95-th percentile2388.23
Maximum2388.56
Range0.68
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.071918916
Coefficient of variation (CV)3.0115586 × 10-5
Kurtosis0.38724376
Mean2388.0962
Median Absolute Deviation (MAD)0.05
Skewness0.46979242
Sum49268812
Variance0.0051723304
MonotonicityNot monotonic
2023-03-14T16:25:00.485172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2388.1 1164
 
5.6%
2388.09 1144
 
5.5%
2388.08 1129
 
5.5%
2388.11 1127
 
5.5%
2388.07 1112
 
5.4%
2388.12 1099
 
5.3%
2388.06 1005
 
4.9%
2388.05 987
 
4.8%
2388.13 976
 
4.7%
2388.04 952
 
4.6%
Other values (46) 9936
48.2%
ValueCountFrequency (%)
2387.88 1
 
< 0.1%
2387.89 1
 
< 0.1%
2387.9 1
 
< 0.1%
2387.91 2
 
< 0.1%
2387.92 12
 
0.1%
2387.93 19
 
0.1%
2387.94 54
 
0.3%
2387.95 95
0.5%
2387.96 170
0.8%
2387.97 219
1.1%
ValueCountFrequency (%)
2388.56 1
 
< 0.1%
2388.55 1
 
< 0.1%
2388.54 1
 
< 0.1%
2388.49 1
 
< 0.1%
2388.44 1
 
< 0.1%
2388.39 2
 
< 0.1%
2388.37 3
 
< 0.1%
2388.36 6
< 0.1%
2388.35 7
< 0.1%
2388.34 8
< 0.1%
Distinct6078
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8143.7527
Minimum8099.94
Maximum8293.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-03-14T16:25:00.635287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8099.94
5-th percentile8122.505
Q18133.245
median8140.54
Q38148.31
95-th percentile8181.405
Maximum8293.72
Range193.78
Interquartile range (IQR)15.065

Descriptive statistics

Standard deviation19.076176
Coefficient of variation (CV)0.0023424306
Kurtosis8.8546645
Mean8143.7527
Median Absolute Deviation (MAD)7.54
Skewness2.3725536
Sum1.6801376 × 108
Variance363.90049
MonotonicityNot monotonic
2023-03-14T16:25:00.784385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8138.89 17
 
0.1%
8141.85 17
 
0.1%
8136.89 16
 
0.1%
8140.79 15
 
0.1%
8140.65 15
 
0.1%
8140.49 15
 
0.1%
8140.33 15
 
0.1%
8140.89 15
 
0.1%
8136.69 15
 
0.1%
8140.97 15
 
0.1%
Other values (6068) 20476
99.2%
ValueCountFrequency (%)
8099.94 1
< 0.1%
8101.49 1
< 0.1%
8102.82 1
< 0.1%
8103.27 1
< 0.1%
8103.77 1
< 0.1%
8103.98 1
< 0.1%
8104.46 1
< 0.1%
8104.78 1
< 0.1%
8104.82 1
< 0.1%
8105.22 1
< 0.1%
ValueCountFrequency (%)
8293.72 1
< 0.1%
8290.25 1
< 0.1%
8289.63 1
< 0.1%
8288.26 1
< 0.1%
8282.5 1
< 0.1%
8279.86 1
< 0.1%
8279.79 1
< 0.1%
8276.2 1
< 0.1%
8274.65 1
< 0.1%
8273.15 1
< 0.1%

(Bypass Ratio)
Real number (ℝ)

Distinct1918
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4421456
Minimum8.3249
Maximum8.5848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-03-14T16:25:00.934099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8.3249
5-th percentile8.3859
Q18.4149
median8.4389
Q38.4656
95-th percentile8.511
Maximum8.5848
Range0.2599
Interquartile range (IQR)0.0507

Descriptive statistics

Standard deviation0.037505038
Coefficient of variation (CV)0.0044425955
Kurtosis-0.12143
Mean8.4421456
Median Absolute Deviation (MAD)0.0252
Skewness0.38825858
Sum174169.91
Variance0.0014066279
MonotonicityNot monotonic
2023-03-14T16:25:01.080426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.4309 38
 
0.2%
8.4318 37
 
0.2%
8.4468 36
 
0.2%
8.4442 35
 
0.2%
8.4128 34
 
0.2%
8.4453 32
 
0.2%
8.4446 32
 
0.2%
8.4371 31
 
0.2%
8.4209 31
 
0.2%
8.4226 31
 
0.2%
Other values (1908) 20294
98.4%
ValueCountFrequency (%)
8.3249 1
< 0.1%
8.3279 1
< 0.1%
8.3303 1
< 0.1%
8.3358 2
< 0.1%
8.3365 1
< 0.1%
8.3387 1
< 0.1%
8.34 1
< 0.1%
8.3409 1
< 0.1%
8.3427 1
< 0.1%
8.3428 1
< 0.1%
ValueCountFrequency (%)
8.5848 1
< 0.1%
8.5836 1
< 0.1%
8.5678 1
< 0.1%
8.5671 1
< 0.1%
8.5668 1
< 0.1%
8.5665 1
< 0.1%
8.5654 1
< 0.1%
8.5648 1
< 0.1%
8.5646 1
< 0.1%
8.5641 1
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
0.03
20631 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters82524
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.03
2nd row0.03
3rd row0.03
4th row0.03
5th row0.03

Common Values

ValueCountFrequency (%)
0.03 20631
100.0%

Length

2023-03-14T16:25:01.208632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:25:01.321813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
0 41262
50.0%
. 20631
25.0%
3 20631
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61893
75.0%
Other Punctuation 20631
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41262
66.7%
3 20631
33.3%
Other Punctuation
ValueCountFrequency (%)
. 20631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 82524
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41262
50.0%
. 20631
25.0%
3 20631
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41262
50.0%
. 20631
25.0%
3 20631
25.0%

(Bleed Enthalpy)
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean393.21065
Minimum388
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-03-14T16:25:01.410695image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum388
5-th percentile391
Q1392
median393
Q3394
95-th percentile396
Maximum400
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.548763
Coefficient of variation (CV)0.0039387616
Kurtosis-0.039174043
Mean393.21065
Median Absolute Deviation (MAD)1
Skewness0.35312566
Sum8112329
Variance2.3986669
MonotonicityNot monotonic
2023-03-14T16:25:01.517569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
393 5445
26.4%
392 4578
22.2%
394 4063
19.7%
395 2339
11.3%
391 2022
 
9.8%
396 1185
 
5.7%
390 452
 
2.2%
397 436
 
2.1%
398 72
 
0.3%
389 30
 
0.1%
Other values (3) 9
 
< 0.1%
ValueCountFrequency (%)
388 1
 
< 0.1%
389 30
 
0.1%
390 452
 
2.2%
391 2022
 
9.8%
392 4578
22.2%
393 5445
26.4%
394 4063
19.7%
395 2339
11.3%
396 1185
 
5.7%
397 436
 
2.1%
ValueCountFrequency (%)
400 1
 
< 0.1%
399 7
 
< 0.1%
398 72
 
0.3%
397 436
 
2.1%
396 1185
 
5.7%
395 2339
11.3%
394 4063
19.7%
393 5445
26.4%
392 4578
22.2%
391 2022
 
9.8%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
2388
20631 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters82524
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2388
2nd row2388
3rd row2388
4th row2388
5th row2388

Common Values

ValueCountFrequency (%)
2388 20631
100.0%

Length

2023-03-14T16:25:01.623652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:25:01.723640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2388 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
8 41262
50.0%
2 20631
25.0%
3 20631
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82524
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 41262
50.0%
2 20631
25.0%
3 20631
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 82524
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 41262
50.0%
2 20631
25.0%
3 20631
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82524
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 41262
50.0%
2 20631
25.0%
3 20631
25.0%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
100.0
20631 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters103155
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row100.0

Common Values

ValueCountFrequency (%)
100.0 20631
100.0%

Length

2023-03-14T16:25:01.813831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-14T16:25:01.917288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
100.0 20631
100.0%

Most occurring characters

ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 82524
80.0%
Other Punctuation 20631
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 61893
75.0%
1 20631
 
25.0%
Other Punctuation
ValueCountFrequency (%)
. 20631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103155
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 61893
60.0%
1 20631
 
20.0%
. 20631
 
20.0%
Distinct120
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.816271
Minimum38.14
Maximum39.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-03-14T16:25:02.021530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum38.14
5-th percentile38.49
Q138.7
median38.83
Q338.95
95-th percentile39.09
Maximum39.43
Range1.29
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.18074643
Coefficient of variation (CV)0.0046564604
Kurtosis-0.11282911
Mean38.816271
Median Absolute Deviation (MAD)0.12
Skewness-0.3584452
Sum800818.48
Variance0.032669271
MonotonicityNot monotonic
2023-03-14T16:25:02.173707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.86 485
 
2.4%
38.89 476
 
2.3%
38.82 472
 
2.3%
38.87 460
 
2.2%
38.85 458
 
2.2%
38.83 457
 
2.2%
38.84 455
 
2.2%
38.88 452
 
2.2%
38.81 447
 
2.2%
38.8 447
 
2.2%
Other values (110) 16022
77.7%
ValueCountFrequency (%)
38.14 1
 
< 0.1%
38.16 1
 
< 0.1%
38.18 1
 
< 0.1%
38.19 1
 
< 0.1%
38.2 1
 
< 0.1%
38.21 1
 
< 0.1%
38.22 3
 
< 0.1%
38.23 5
< 0.1%
38.24 7
< 0.1%
38.25 9
< 0.1%
ValueCountFrequency (%)
39.43 1
 
< 0.1%
39.41 1
 
< 0.1%
39.34 1
 
< 0.1%
39.32 1
 
< 0.1%
39.31 2
 
< 0.1%
39.3 2
 
< 0.1%
39.29 3
 
< 0.1%
39.28 1
 
< 0.1%
39.27 10
< 0.1%
39.26 7
< 0.1%
Distinct4745
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.289705
Minimum22.8942
Maximum23.6184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-03-14T16:25:02.328707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22.8942
5-th percentile23.09345
Q123.2218
median23.2979
Q323.3668
95-th percentile23.4535
Maximum23.6184
Range0.7242
Interquartile range (IQR)0.145

Descriptive statistics

Standard deviation0.10825087
Coefficient of variation (CV)0.0046480139
Kurtosis-0.11703945
Mean23.289705
Median Absolute Deviation (MAD)0.0724
Skewness-0.35037496
Sum480489.91
Variance0.011718252
MonotonicityNot monotonic
2023-03-14T16:25:02.470287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.3222 23
 
0.1%
23.3029 17
 
0.1%
23.2896 16
 
0.1%
23.3725 16
 
0.1%
23.371 15
 
0.1%
23.3491 15
 
0.1%
23.3497 15
 
0.1%
23.3315 15
 
0.1%
23.3002 15
 
0.1%
23.3309 15
 
0.1%
Other values (4735) 20469
99.2%
ValueCountFrequency (%)
22.8942 1
< 0.1%
22.9071 1
< 0.1%
22.9122 1
< 0.1%
22.9305 1
< 0.1%
22.9333 1
< 0.1%
22.9337 1
< 0.1%
22.9364 1
< 0.1%
22.9396 2
< 0.1%
22.9398 1
< 0.1%
22.9402 1
< 0.1%
ValueCountFrequency (%)
23.6184 1
< 0.1%
23.6127 1
< 0.1%
23.6064 1
< 0.1%
23.6005 1
< 0.1%
23.5983 1
< 0.1%
23.589 1
< 0.1%
23.5862 2
< 0.1%
23.5858 1
< 0.1%
23.5825 1
< 0.1%
23.5791 1
< 0.1%

Interactions

2023-03-14T16:24:51.066200image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:00.132020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:03.174727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:05.961219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:09.079660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:12.014015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:14.792402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:17.700829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:20.463966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:23.554395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:26.558991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:30.082147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:33.484760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:36.227629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:39.583707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:42.533564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:45.357752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:48.253430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:51.221402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:00.301994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:03.315217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:06.097331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:09.219024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:12.164623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:14.930386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:17.841853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:20.603871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:23.707979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:26.673129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:30.249024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:33.623288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:36.384368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:39.769913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:42.687604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:45.495893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:48.393708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:51.396765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:00.459412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:03.462496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:06.404052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:09.367764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:12.326229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:15.073786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:17.989808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:20.800463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:23.870733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:26.843770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:30.415722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:33.784306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:36.681661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:39.930822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:43.042501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:45.634369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:48.530155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:51.559569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:00.624121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:03.622550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:06.554499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:09.513936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:12.471922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:15.246151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:18.147430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:20.979716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:24.041216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:26.980525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:30.555586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:33.939104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:36.878321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:40.083890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:43.173061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:45.792396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:48.676028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:51.710070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:00.768643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:03.781404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:06.903841image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:09.666315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:12.628171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:15.400667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:18.303672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:21.136379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:24.205320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:27.131797image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:30.706764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:34.094470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:37.201042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:40.250634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:43.349294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:45.941659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:48.859983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:51.862715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:00.940904image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:03.933327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:07.044014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:09.812540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:12.787357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:15.539951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:18.463794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:21.296653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:24.368903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-03-14T16:24:33.168120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:35.897754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:39.263180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:42.197094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:45.061490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:47.943547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:50.737078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:53.851113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:03.015261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:05.802273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:08.922843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:11.861956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:14.615826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:17.537151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:20.305382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:23.385891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:26.194768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:29.888909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:33.323821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:36.044859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:39.420144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:42.350089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:45.199909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:48.089302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-14T16:24:50.892078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-03-14T16:25:02.627555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
unit_numbertime_cyclessetting_1setting_2(Total temperature at LPC outlet) (â—¦R)(Total temperature at HPC outlet) (â—¦R)(Total temperature at LPT outlet) (â—¦R)(Total pressure at HPC outlet) (psia)(Physical fan speed) (rpm)(Physical core speed) (rpm)(Static pressure at HPC outlet) (psia)(Ratio of fuel flow to Ps30) (pps/psia)(Corrected fan speed) (rpm)(Corrected core speed) (rpm)(Bypass Ratio)(Bleed Enthalpy)(HPT coolant bleed (lbm/s))(LPT coolant bleed (lbm/s))(Total pressure in bypass-duct) (psia)
unit_number1.0000.058-0.020-0.0060.0140.0150.025-0.0330.043-0.0230.025-0.0310.046-0.0440.0210.014-0.018-0.0160.048
time_cycles0.0581.000-0.0060.0120.5340.5290.605-0.5780.4550.4020.615-0.5930.4580.2940.5710.553-0.568-0.5700.108
setting_1-0.020-0.0061.0000.0080.009-0.0070.009-0.008-0.003-0.0040.0080.000-0.003-0.0030.0050.003-0.006-0.0110.003
setting_2-0.0060.0120.0081.0000.0090.0090.017-0.0170.013-0.0180.012-0.0100.019-0.0190.0140.013-0.011-0.0100.000
(Total temperature at LPC outlet) (â—¦R)0.0140.5340.0090.0091.0000.5760.693-0.6800.6500.1040.717-0.7060.649-0.0190.6510.605-0.640-0.6430.146
(Total temperature at HPC outlet) (â—¦R)0.0150.529-0.0070.0090.5761.0000.651-0.6430.5920.1430.670-0.6600.5930.0290.6130.575-0.599-0.6100.125
(Total temperature at LPT outlet) (â—¦R)0.0250.6050.0090.0170.6930.6511.000-0.7740.7390.1070.812-0.8000.737-0.0350.7320.678-0.727-0.7200.170
(Total pressure at HPC outlet) (psia)-0.033-0.578-0.008-0.017-0.680-0.643-0.7741.000-0.755-0.056-0.8060.794-0.7540.086-0.726-0.6740.7160.7150.184
(Physical fan speed) (rpm)0.0430.455-0.0030.0130.6500.5920.739-0.7551.000-0.1790.777-0.7750.807-0.3260.6890.618-0.677-0.6770.180
(Physical core speed) (rpm)-0.0230.402-0.004-0.0180.1040.1430.107-0.056-0.1791.0000.083-0.046-0.1820.8860.1150.154-0.111-0.1140.054
(Static pressure at HPC outlet) (psia)0.0250.6150.0080.0120.7170.6700.812-0.8060.7770.0831.000-0.8320.776-0.0660.7580.698-0.750-0.7500.199
(Ratio of fuel flow to Ps30) (pps/psia)-0.031-0.5930.000-0.010-0.706-0.660-0.8000.794-0.775-0.046-0.8321.000-0.7770.101-0.745-0.6840.7340.7360.188
(Corrected fan speed) (rpm)0.0460.458-0.0030.0190.6490.5930.737-0.7540.807-0.1820.776-0.7771.000-0.3280.6870.618-0.676-0.6770.186
(Corrected core speed) (rpm)-0.0440.294-0.003-0.019-0.0190.029-0.0350.086-0.3260.886-0.0660.101-0.3281.000-0.0190.0320.0190.0200.082
(Bypass Ratio)0.0210.5710.0050.0140.6510.6130.732-0.7260.6890.1150.758-0.7450.687-0.0191.0000.643-0.682-0.6780.173
(Bleed Enthalpy)0.0140.5530.0030.0130.6050.5750.678-0.6740.6180.1540.698-0.6840.6180.0320.6431.000-0.626-0.6320.146
(HPT coolant bleed (lbm/s))-0.018-0.568-0.006-0.011-0.640-0.599-0.7270.716-0.677-0.111-0.7500.734-0.6760.019-0.682-0.6261.0000.6680.162
(LPT coolant bleed (lbm/s))-0.016-0.570-0.011-0.010-0.643-0.610-0.7200.715-0.677-0.114-0.7500.736-0.6770.020-0.678-0.6320.6681.0000.154
(Total pressure in bypass-duct) (psia)0.0480.1080.0030.0000.1460.1250.1700.1840.1800.0540.1990.1880.1860.0820.1730.1460.1620.1541.000

Missing values

2023-03-14T16:24:54.120095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-14T16:24:54.812714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

unit_numbertime_cyclessetting_1setting_2setting_3(Total temperature at fan inlet) (â—¦R)(Total temperature at LPC outlet) (â—¦R)(Total temperature at HPC outlet) (â—¦R)(Total temperature at LPT outlet) (â—¦R)(Pressure at fan inlet) (psia)(Total pressure in bypass-duct) (psia)(Total pressure at HPC outlet) (psia)(Physical fan speed) (rpm)(Physical core speed) (rpm)(Engine pressure ratio(P50/P2)(Static pressure at HPC outlet) (psia)(Ratio of fuel flow to Ps30) (pps/psia)(Corrected fan speed) (rpm)(Corrected core speed) (rpm)(Bypass Ratio)(Burner fuel-air ratio)(Bleed Enthalpy)(Demanded fan speed)(Demanded corrected fan speed)(HPT coolant bleed (lbm/s))(LPT coolant bleed (lbm/s))
011-0.0007-0.0004100.0518.67641.821589.701400.6014.6221.61554.362388.069046.191.347.47521.662388.028138.628.41950.033922388100.039.0623.4190
1120.0019-0.0003100.0518.67642.151591.821403.1414.6221.61553.752388.049044.071.347.49522.282388.078131.498.43180.033922388100.039.0023.4236
213-0.00430.0003100.0518.67642.351587.991404.2014.6221.61554.262388.089052.941.347.27522.422388.038133.238.41780.033902388100.038.9523.3442
3140.00070.0000100.0518.67642.351582.791401.8714.6221.61554.452388.119049.481.347.13522.862388.088133.838.36820.033922388100.038.8823.3739
415-0.0019-0.0002100.0518.67642.371582.851406.2214.6221.61554.002388.069055.151.347.28522.192388.048133.808.42940.033932388100.038.9023.4044
516-0.0043-0.0001100.0518.67642.101584.471398.3714.6221.61554.672388.029049.681.347.16521.682388.038132.858.41080.033912388100.038.9823.3669
6170.00100.0001100.0518.67642.481592.321397.7714.6221.61554.342388.029059.131.347.36522.322388.038132.328.39740.033922388100.039.1023.3774
718-0.00340.0003100.0518.67642.561582.961400.9714.6221.61553.852388.009040.801.347.24522.472388.038131.078.40760.033912388100.038.9723.3106
8190.00080.0001100.0518.67642.121590.981394.8014.6221.61553.692388.059046.461.347.29521.792388.058125.698.37280.033922388100.039.0523.4066
9110-0.00330.0001100.0518.67641.711591.241400.4614.6221.61553.592388.059051.701.347.03521.792388.068129.388.42860.033932388100.038.9523.4694
unit_numbertime_cyclessetting_1setting_2setting_3(Total temperature at fan inlet) (â—¦R)(Total temperature at LPC outlet) (â—¦R)(Total temperature at HPC outlet) (â—¦R)(Total temperature at LPT outlet) (â—¦R)(Pressure at fan inlet) (psia)(Total pressure in bypass-duct) (psia)(Total pressure at HPC outlet) (psia)(Physical fan speed) (rpm)(Physical core speed) (rpm)(Engine pressure ratio(P50/P2)(Static pressure at HPC outlet) (psia)(Ratio of fuel flow to Ps30) (pps/psia)(Corrected fan speed) (rpm)(Corrected core speed) (rpm)(Bypass Ratio)(Burner fuel-air ratio)(Bleed Enthalpy)(Demanded fan speed)(Demanded corrected fan speed)(HPT coolant bleed (lbm/s))(LPT coolant bleed (lbm/s))
20621100191-0.0005-0.0000100.0518.67643.691610.871427.1914.6221.61551.782388.269068.901.348.07519.802388.288143.568.50920.033982388100.038.3923.1218
20622100192-0.00090.0001100.0518.67643.531601.231419.4814.6221.61551.142388.179060.451.348.18520.592388.218143.468.48920.033972388100.038.5623.0770
20623100193-0.00010.0002100.0518.67643.091599.811428.9314.6221.61552.042388.299067.571.348.19520.112388.198142.028.54240.033972388100.038.4723.0230
20624100194-0.00110.0003100.0518.67643.721597.291427.4114.6221.61551.992388.239068.851.348.12519.552388.228139.678.52150.033942388100.038.3823.1324
20625100195-0.0002-0.0001100.0518.67643.411600.041431.9014.6221.61551.422388.239069.691.348.22519.712388.288142.908.55190.033942388100.038.1423.1923
20626100196-0.0004-0.0003100.0518.67643.491597.981428.6314.6221.61551.432388.199065.521.348.07519.492388.268137.608.49560.033972388100.038.4922.9735
20627100197-0.0016-0.0005100.0518.67643.541604.501433.5814.6221.61550.862388.239065.111.348.04519.682388.228136.508.51390.033952388100.038.3023.1594
206281001980.00040.0000100.0518.67643.421602.461428.1814.6221.61550.942388.249065.901.348.09520.012388.248141.058.56460.033982388100.038.4422.9333
20629100199-0.00110.0003100.0518.67643.231605.261426.5314.6221.61550.682388.259073.721.348.39519.672388.238139.298.53890.033952388100.038.2923.0640
20630100200-0.0032-0.0005100.0518.67643.851600.381432.1414.6221.61550.792388.269061.481.348.20519.302388.268137.338.50360.033962388100.038.3723.0522